Aircraft Temperatures

Early in the MERRA reanalysis period, aircraft observations are sparse, but increase in time, eventually providing a significant amount of conventional observations. Cardinali et al. (2003) identified biases in aircraft temperature observations, and Ballish and Kumar (2008) further examined the biases in each type of commercial aircraft. Figure 1 shows an 2001-2009 mean bias between collocated aircraft and radiosonde 200mb temperature, over the United  States. Almost everywhere, aircraft are warmer than the radiosonde observations.

Figure 1 Collocated aircraft/RAOB at 200mb temperature (K) differences assimilated in MERRA  averaged from 2001-2012. (computed from the differences of each observaitons background departure)

Figure 2 shows the monthly mean difference between collocated radiosonde and aircraft observations assimilated in MERRA over the U. S., while the dots show the number of collocations (thou/yr).  While the data for these figures are binned and gridded, area and monthly averaging include weighting for the number of observations. Early in the reanalysis, there are lower numbers of aircraft observations, and the differences reflect that with more monthly variability. In 1990-1991, increasing number of observations increase the distribution of data, and the warm bias converges. After 1996, there is an exaggeration in the annual cycle, where the summer aircraft observations get even warmer. However, every month is a positive difference.

Figure 2 Time series of monthly mean differences of 200mb temperature collocations over the United States (Aircraft minus RAOB OmF, in red, K). The black dots indicate number of collocations each year (in thousands, right axis).

The increasing number of collocations reflects the increase in availability of aircraft observations. There are many more aircraft observations being assimilated away from the vicinity of the radiosondes. The number of observations then influences the data assimilation, where the analysis is drawn toward the aircraft data. Figure 3 shows the time series of background departure for collocated radiosonde and aircraft 200mb temperature. As the aircraft observations increase in number, their background departure decreases (this also holds for the RMS of the background departure).

Figure 3 Time series of monthly mean background departure (OmF) of the collocated RAOB (black) and Aircraft (red) 200mb temperatures (K, left axis). The black dots indicate number of collocations each year (in thousands, right axis).

Both Cardinali et al (2003) and Ballish and Kumar (2008) have suggested bias corrections for commercial aircraft temperature data, using more limited comparisons than these. ECMWF has implemented a bias correction in their forecast system.

Reanalyses trends

One of the most important topics and calculations in climate science is trend, aiming to determine long term changes. Significant issues exist in the observational record, and methods correcting the problems themselves need to be explained and verified. In a recent update to the U.S. Historical Climate Network (HCN) station data, Vose et al. compare the observational record against several reanalyses near surface air temperature and their ensemble. In looking at the continental United States, their Figure 1 shows the revised HCN trend is larger than the uncorrected data, but also remarkably close to the ensemble of the reanalyses. Also, despite the differences in trends of the reanalyses, there is very good agreement in the reanalyses interannual variability around the trends (their figure 3). The bottom line is that the corrections to HCN are in agreement with reanalyses (all are statistically significant warm trends), but it is noted that this is not a validation of the corrections.

The spatial distribution of reanalysis trend relative to the HCN trend shows substantial local variations among the reanalyses (their figure 4). So, while the observational forcing imposed on reanalyses can influence the large scale features, the model predictions used to make the analyses impart  some uncertainty related to the model physical parameterizations. If the model errors are random, the ensemble should then minimize the error. Errors that are systematic among all reanalyses would persist in the ensemble.

This paper demonstrates some important points about reanalyses. Any one reanalysis may have uncertainty in any given research project. Multiple reanalyses can help identify these uncertainties and perhaps the background model biases in the reanalysis. However, the reanalyses output variables being compared must have equivalent formulations to take advantage of the availability of the current modern reanalyses through such intercomparisons. Likewise, on hourly surface output in MERRA and CFSR were useful in this study.

Return of the MERRA Blog

As many readers will understand, the demands on time can be many, and there is never enough time to do all that you want to do. Being spread thin for some time, the MERRA blog fell into neglect. However, with a lot of interesting things going on, from research with MERRA to formulating plans for subsequent reanalyses (and noticing in the last month a fair number of hits from around the world), I will be trying hard to find time to make regular posts.

The original purpose of the blog was to 1) follow the development of the MERRA system and eventually the production of data and then 2) follow the research that was being done with MERRA and other reanalyses. #2 never fully materialized, but there is a lot of work being published on reanalyses lately.  So, for the near future, summaries of published research or perhaps topical discussions involving several papers/reports will likely be the regular topic. However, new reanalysis development is on the horizon, as well.

Mostly, I figure that the hits on the MERRA blog are coming from internet searches for hard to find information, now that the MERRA overview paper is printed. This is an important issue, as users need to know if a reanalysis is applicable to a research topic, and its strengths and weaknesses. So, perhaps this can open the communication, just a little more.